Syntax-Based Alignment Supervised or Unsupervised.pdf

Syntax-Based Alignment: Supervised or Unsupervised? Hao Zhang and Daniel Gildea Computer Science Department University of Rochester Rochester, NY 14627 Abstract Tree-based approaches to alignment model translation as a sequence of probabilistic op- erations transforming the syntactic parse tree of a sentence in one language into that of the other. The trees may be learned directly from parallel corpora (Wu, 1997), or provided by a parser trained on hand-annotated treebanks (Ya- mada and Knight, 2001). In this paper, we compare these approaches on Chinese-English and French-English datasets, and find that au- tomatically derived trees result in better agree- ment with human-annotated word-level align- ments for unseen test data. 1 Introduction Statistical approaches to machine translation, pio- neered by Brown et al. (1990), estimate parame- ters for a probabilistic model of word-to-word cor- respondences and word re-orderings directly from large corpora of parallel bilingual text. In re- cent years, a number of syntactically motivated ap- proaches to statistical machine translation have been proposed. These approaches assign a parallel tree structure to the two sides of each sentence pair, and model the translation process with reordering oper- ations defined on the tree structure. The tree-based approach allows us to represent the fact that syn- tactic constituents tend to move as unit, as well as systematic differences in word order in the gram- mars of the two languages. Furthermore, the tree structure allows us to make probabilistic indepen- dence assumptions that result in polynomial time algorithms for estimating a translation model from parallel training data, and for finding the highest probability translation given a new sentence. Wu (1997) modeled the reordering process with binary branching trees, where each production could be either in the same or in reverse order going from source to target language. The trees of Wu’s Inversion Transduction Grammar wer